IAM datasets implemented.

1 files changed, 0 insertions, 78 deletions

diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py
index 8df94f3..8c7b085 100644
--- a/src/training/trainer/callbacks/base.py
+++ b/src/training/trainer/callbacks/base.py
@@ -168,81 +168,3 @@ class CallbackList:
     def __iter__(self) -> iter:
         """Iter function for callback list."""
         return iter(self._callbacks)
-
-
-class Checkpoint(Callback):
-    """Saving model parameters at the end of each epoch."""
-
-    mode_dict = {
-        "min": torch.lt,
-        "max": torch.gt,
-    }
-
-    def __init__(
-        self, monitor: str = "accuracy", mode: str = "auto", min_delta: float = 0.0
-    ) -> None:
-        """Monitors a quantity that will allow us to determine the best model weights.
-
-        Args:
-            monitor (str): Name of the quantity to monitor. Defaults to "accuracy".
-            mode (str): Description of parameter `mode`. Defaults to "auto".
-            min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
-
-        """
-        super().__init__()
-        self.monitor = monitor
-        self.mode = mode
-        self.min_delta = torch.tensor(min_delta)
-
-        if mode not in ["auto", "min", "max"]:
-            logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.")
-
-            self.mode = "auto"
-
-        if self.mode == "auto":
-            if "accuracy" in self.monitor:
-                self.mode = "max"
-            else:
-                self.mode = "min"
-            logger.debug(
-                f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}."
-            )
-
-        torch_inf = torch.tensor(np.inf)
-        self.min_delta *= 1 if self.monitor_op == torch.gt else -1
-        self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
-
-    @property
-    def monitor_op(self) -> float:
-        """Returns the comparison method."""
-        return self.mode_dict[self.mode]
-
-    def on_epoch_end(self, epoch: int, logs: Dict) -> None:
-        """Saves a checkpoint for the network parameters.
-
-        Args:
-            epoch (int): The current epoch.
-            logs (Dict): The log containing the monitored metrics.
-
-        """
-        current = self.get_monitor_value(logs)
-        if current is None:
-            return
-        if self.monitor_op(current - self.min_delta, self.best_score):
-            self.best_score = current
-            is_best = True
-        else:
-            is_best = False
-
-        self.model.save_checkpoint(is_best, epoch, self.monitor)
-
-    def get_monitor_value(self, logs: Dict) -> Union[float, None]:
-        """Extracts the monitored value."""
-        monitor_value = logs.get(self.monitor)
-        if monitor_value is None:
-            logger.warning(
-                f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available"
-                + f"metrics are: {','.join(list(logs.keys()))}"
-            )
-            return None
-        return monitor_value